Research

IntervalCoach learns how each athlete responds to training, night by night. Pooled across everyone, that data answers questions about training and recovery, and the answers change how the coach behaves.

Studies

  1. How many mornings does it take to know how you recover?

    What we found
    Modelling how the effects of consecutive sessions overlap turns about a hundred mornings per athlete into usable evidence, instead of the four or five a clean gap between sessions allows. On mornings the model had not seen, the new curve is more accurate for about six athletes in ten.
    What we changed
    Recovery depth is now fitted individually for every athlete, and the recovery rate for every athlete whose data supports one. Where it does not, the population value is used and labelled as such.
    Read the study →

  2. Can you see illness coming? What the overnight data shows

    What we found
    Recovery outlasts the illness. Overnight resting heart rate and HRV are most disrupted around four days after a sick spell ends, and the longer the illness lasted, the deeper the disruption: after a week or more ill it is still clearly there in the second week. Before the illness, the average athlete's numbers barely move.
    What we changed
    The return to training after illness lasts at least five days, scales with how long you were ill, and holds the easing until your resting heart rate and HRV are back at your own normal, whatever the calendar says.
    Read the study →

  3. Do fitter athletes recover faster? What our recovery curves show

    What we found
    Fitness changes how hard a session hits, not how fast you recover from it. With each athlete's curve fitted from their own mornings, the overnight hit from a hard session is roughly 40% smaller in the fittest athletes than in the least fit, falling steadily with fitness, while the time it takes to clear shows no trend with fitness at all.
    What we changed
    Each athlete's recovery curve fits the size of a session's hit from their own mornings, and keeps the time it takes to clear tied to the type of session. That is why a less trained athlete's plan leaves more room around hard days, and why that room closes as they get fitter.
    Read the study →

How we run them

Measured against your own normal

Every signal is read as a deviation from that athlete’s own baseline, so athletes with very different resting values can be pooled.

Every study changes the product

A study ends with what we changed because of it. That is the reason we run them.

Re-run as the data grows

Each study is repeated on fresh data before it is published, and updated when a later run moves the result.

Observational, and labelled that way

These read our own athletes’ data. They are not peer-reviewed trials, and they describe our population.